Introducing cyankiwi AWQ 4-bit Quantization — 26.05 update
r/LocalLLaMA
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Generative AI
Open Source AI
AI Research
In standard AWQ, per-channel scales and quantization ranges are picked in separate steps: scales first, then the quantization parameters. But they're not independent, i.e., the rounding error from one depends on the choice of the other, so optimizing them in sequence leaves quality on the table. Our cyankiwi AWQ 26.05 update jointly fits scales and quantization ranges against a reconstruction objective. We benchmarked cyankiwi AWQ 26.05 update against every major 4-bit method on Llama-3 as examples, measuring KL Divergence vs the BF16 baseline on GPQA Diamond responses.